class_docstr: The RMSProp optimizer.
  
  Two versions of RMSProp are implemented:
  
  If ``centered=False``, we follow
  http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf by
  Tieleman & Hinton, 2012.
  For details of the update algorithm see :class:`~mxnet.ndarray.rmsprop_update`.
  
  If ``centered=True``, we follow http://arxiv.org/pdf/1308.0850v5.pdf (38)-(45)
  by Alex Graves, 2013.
  For details of the update algorithm see :class:`~mxnet.ndarray.rmspropalex_update`.
  
  This optimizer accepts the following parameters in addition to those accepted
  by :class:`.Optimizer`.
  
  Parameters
  ----------
  learning_rate : float, default 0.001
      The initial learning rate. If None, the optimization will use the
      learning rate from ``lr_scheduler``. If not None, it will overwrite
      the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``
      is also None, then it will be set to 0.01 by default.
  rho: float, default 0.9
      A decay factor of moving average over past squared gradient.
  momentum: float, default 0.9
      Heavy ball momentum factor. Only used if `centered`=``True``.
  epsilon : float, default 1e-8
      Small value to avoid division by 0.
  centered : bool, default False
      Flag to control which version of RMSProp to use.::
  
          True: will use Graves's version of `RMSProp`,
          False: will use Tieleman & Hinton's version of `RMSProp`.
  
  clip_weights : float, optional
      Clips weights into range ``[-clip_weights, clip_weights]``.
  use_fused_step : bool, default True
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False, step is called,
      otherwise, fused_step is called.
code: def __init__(
      self,
      learning_rate=0.001,
      rho=0.9,
      momentum=0.9,
      epsilon=1e-8,
      centered=False,
      clip_weights=None,
      use_fused_step=True,
      **kwargs,
  ):
      if kwargs.get("gamma1") is not None:
          raise DeprecationWarning(
              "parameter 'gamma1' is deprecated. Please use 'rho' instead..."
          )
      if kwargs.get("gamma2") is not None:
          raise DeprecationWarning(
              "parameter 'gamma2' is deprecated. Please use 'momentum' instead..."
          )
      super(RMSProp, self).__init__(
          learning_rate=learning_rate,
          use_fused_step=use_fused_step,
          **kwargs,
      )
      self.rho = rho
      self.momentum = momentum
      self.centered = centered
      self.epsilon = epsilon
      self.clip_weights = clip_weights
  
file_path: mxnet/python/mxnet/optimizer/rmsprop.py
imports: from __future__ import absolute_import
  from ..ndarray import zeros, clip, sqrt, square
  from ..ndarray import rmsprop_update, rmspropalex_update
  from .optimizer import Optimizer, register
label_desc: Use image node_id 1 to create a new RMSProp object from inherited base classes: Optimizer with example: obj = RMSProp(learning_rate, rho, momentum, epsilon, centered, clip_weights, use_fused_step)